MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.
In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. We will cover:
- Representation, over-fitting, regularization, generalization, VC dimension;
- Clustering, classification, recommender problems, probabilistic modeling, reinforcement learning;
- On-line algorithms, support vector machines, and neural networks/deep learning.
Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.
This course is part of the MITx MicroMasters Program in Statistics and Data Science.
What you'll learn
- Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning
- Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models
- Choose suitable models for different applications
- Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering.
Syllabus
Lectures :
- Introduction
- Linear classifiers, separability, perceptron algorithm
- Maximum margin hyperplane, loss, regularization
- Stochastic gradient descent, over-fitting, generalization
- Linear regression
- Recommender problems, collaborative filtering
- Non-linear classification, kernels
- Learning features, Neural networks
- Deep learning, back propagation
- Recurrent neural networks
- Recurrent neural networks
- Generalization, complexity, VC-dimension
- Unsupervised learning: clustering
- Generative models, mixtures
- Mixtures and the EM algorithm
- Learning to control: Reinforcement learning
- Reinforcement learning continued
- Applications: Natural Language Processing
Projects :
- Automatic Review Analyzer
- Digit Recognition with Neural Networks
- Reinforcement Learning
Prerequisites
- 6.00.1x - Introduction to Computer Science and Programming Using Python
or proficiency in Python programming
- 6.431x - Probability - The Science of Uncertainty and Data or equivalent probability theory course
- College-level single and multi-variable calculus
- Vectors and matrices
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.